AI Evaluation Frameworks — From Models to Operators
AI evaluation frameworks target different layers of the stack: governance, model, output, safety. SigRank is the framework for the operator layer — the layer that measures who is best at using the AI.
The landscape of AI evaluation frameworks
AI evaluation frameworks are not interchangeable. Each targets a specific layer of the evaluation stack and answers a different question. Choosing the wrong framework for the layer you need is a common and expensive mistake — a model evaluation framework cannot tell you whether your operators are driving the AI well.
- NIST AI RMF. A governance and risk management framework. Organizes AI risk into Govern, Map, Measure, and Manage functions. Tells you what to evaluate and how to govern it — but does not itself measure operator performance.
- OpenAI Evals. A model evaluation harness. Runs models against test suites and reports scores. Answers: which model performs best on these tasks?
- DeepEval. An LLM output testing framework. Scores outputs against rubrics using LLM-as-judge and metrics. Answers: is this output correct?
- Braintrust. An evaluation platform for LLM applications. Combines offline evals, prompt playground, and observability. Answers: is my LLM application working?
- SigRank. The operator evaluation framework. Measures the humans driving the AI via real token telemetry. Answers: who is best at using the AI?
SigRank — the operator evaluation framework
SigRank is the framework for the operator layer. Four token pillars — input, output, cache-read, cache-write — are captured on-device from real coding sessions across 15+ platforms. The yield metric Υ = cache_read × output / input² measures whether signal is compounding or tokens are burning. Operators are ranked cohort-relative, classified into tiers, and scored over 7-day, 30-day, 90-day, and all-time windows.
The framework is governed, not ad hoc. Snapshots are ed25519-signed and verified server-side, providing cryptographic provenance. No prompt content is ever read — only token counts — so the framework is privacy-preserving by design. The foundation is a published conservation law for language under compression (DOI: 10.5281/zenodo.20029607), with an empirical record and a public transformation harness. It is an open standard, not a proprietary black box.
How the frameworks fit together
The frameworks are complements, not competitors. NIST AI RMF provides the governance structure that tells you to evaluate operators. OpenAI Evals and MMLU tell you which model to deploy. DeepEval and Braintrust tell you whether the output is good. SigRank tells you whether the humans driving the deployed model are driving it well. A mature organization runs frameworks from multiple layers — the operator layer is the one most organizations are missing.
Explore the category
The SigRank Index — Methodology
How operator scores are computed from four token pillars, verified server-side, and ranked. The canonical methodology for the SigRank operator evaluation framework.
The Conservation Law of Commitment
The academic foundation: a published conservation law for language under compression, with Zenodo DOIs and an empirical record. The theory SigRank is built on.
The SigRank Standard
The open operator-evaluation standard: token telemetry, the Yield metric, ed25519-signed snapshots, and cohort-relative ranking. A governed framework, not a proprietary black box.
AI Evaluation — Measuring the Operator, Not Just the Model
The four-layer model of AI evaluation: model, output, safety, operator. SigRank covers the operator layer that other frameworks miss.
FAQ
- What are AI evaluation frameworks?
- Structured methodologies for measuring AI system performance. NIST AI RMF for governance, OpenAI Evals for models, DeepEval for output, SigRank for operators. Each targets a different layer.
- How does SigRank differ from other frameworks?
- Other frameworks measure the model, output, or safety. SigRank measures the operator — the human driving the AI. Four token pillars, the Yield metric, ed25519-signed snapshots. A new category of framework.
- What is the NIST AI RMF?
- A governance and risk management framework with Govern, Map, Measure, and Manage functions. It tells you what to evaluate but does not itself measure operator performance. SigRank provides the governed operator evaluation a NIST “Measure” function requires.
- How do I choose an AI evaluation framework?
- Match the framework to the layer. NIST AI RMF for governance, OpenAI Evals for models, DeepEval for output, SigRank for operators. Most organizations need frameworks from multiple layers.